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Uncertainty-Aware Deep Multi-View Photometric Stereo

Uncertainty-Aware Deep Multi-View Photometric Stereo

26 February 2022
Berk Kaya
Suryansh Kumar
C. Oliveira
V. Ferrari
Luc Van Gool
    3DV
ArXivPDFHTML

Papers citing "Uncertainty-Aware Deep Multi-View Photometric Stereo"

9 / 9 papers shown
Title
MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction
MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction
Wenyuan Zhang
Yixiao Yang
Han Huang
Liang Han
Kanle Shi
Yu-Shen Liu
Zhizhong Han
MDE
55
3
0
24 Mar 2025
Normal-guided Detail-Preserving Neural Implicit Function for High-Fidelity 3D Surface Reconstruction
Normal-guided Detail-Preserving Neural Implicit Function for High-Fidelity 3D Surface Reconstruction
Aarya Patel
Hamid Laga
Ojaswa Sharma
43
1
0
07 Jun 2024
SuperNormal: Neural Surface Reconstruction via Multi-View Normal
  Integration
SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration
Xu Cao
Takafumi Taketomi
3DH
3DGS
19
6
0
08 Dec 2023
A Neural Height-Map Approach for the Binocular Photometric Stereo
  Problem
A Neural Height-Map Approach for the Binocular Photometric Stereo Problem
Fotios Logothetis
Ignas Budvytis
Roberto Cipolla
3DV
19
3
0
10 Nov 2023
MVPSNet: Fast Generalizable Multi-view Photometric Stereo
MVPSNet: Fast Generalizable Multi-view Photometric Stereo
Dongxu Zhao
Daniel Lichy
Pierre-Nicolas Perrin
Jan-Michael Frahm
Soumyadip Sengupta
3DV
19
15
0
18 May 2023
Multi-View Azimuth Stereo via Tangent Space Consistency
Multi-View Azimuth Stereo via Tangent Space Consistency
Xu Cao
Hiroaki Santo
Fumio Okura
Y. Matsushita
3DV
11
8
0
29 Mar 2023
Deep Learning Methods for Calibrated Photometric Stereo and Beyond
Deep Learning Methods for Calibrated Photometric Stereo and Beyond
Yakun Ju
K. Lam
Wuyuan Xie
Huiyu Zhou
Junyu Dong
Boxin Shi
11
34
0
16 Dec 2022
Bayesian Convolutional Neural Networks with Bernoulli Approximate
  Variational Inference
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y. Gal
Zoubin Ghahramani
UQCV
BDL
197
742
0
06 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,109
0
06 Jun 2015
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